Top AI & Machine Learning Jobs

Develop end-to-end data science and digital twin solutions for manufacturing, planning, and supply chain operations. Responsibilities include business problem framing, data analysis, mathematical optimization, discrete event simulation, software and pipeline development, API and UI creation, deployment, user adoption, benchmarking, and continuous improvement across global manufacturing sites.

Own the product backlog and delivery of AI and agentic solutions for procurement and planning. Translate stakeholder needs into requirements, user stories, data requirements, and implementation plans. Collaborate with data scientists and engineers to assess data readiness, develop solutions, and drive products from discovery through deployment, adoption, and value realization. Identify opportunities to improve cost efficiency, productivity, and capital efficiency through AI, automation, and workflow optimization.

Develop an AI-enabled optimization engine for wafer test movement configurations, using machine learning and optimization techniques. Analyze historical semiconductor data, benchmark recommendations, build a web application for visualization and comparison, and validate improvements in test time and tester utilization. The role involves collaboration with Automation, Equipment, and Engineering teams and requires a full-time commitment of at least five months.

Design and build AI-agent-powered test development and qualification platforms for SSD validation. Develop agentic workflows, automated test generation, RAG/GenAI copilots, failure analysis, coverage optimization, and scalable analytics. Collaborate with engineering, data science, and IT to deploy enterprise AI solutions and establish AI governance and benchmarking.

Develop and deploy predictive models and AI/agentic AI solutions using large-scale manufacturing data. Perform EDA, data extraction/cleansing, feature engineering, and statistical modeling. Collaborate with data engineers, manufacturing engineers, UX, and stakeholders to translate insights into operational improvements that boost yield, quality, efficiency, and business value.

Designs, develops, and deploys AI-enabled, cloud-based, full-stack enterprise applications for smart manufacturing. Integrates generative AI, machine learning, and automation into scalable business solutions; builds Angular frontends and .NET APIs and microservices; implements Azure cloud and DevOps practices; optimizes application performance and security; contributes to architecture and modernization initiatives; and mentors engineers while collaborating with stakeholders and technical teams.

Design, develop, and deploy AI-enabled enterprise applications for manufacturing efficiency, quality, and digital transformation. Build full-stack cloud solutions using .NET, Angular, JavaScript, SQL, Azure, Generative AI, and DevOps practices. Develop APIs, microservices, responsive interfaces, and intelligent automation capabilities while supporting architecture modernization, CI/CD, security, performance optimization, and cloud-native deployments. Collaborate with stakeholders and cross-functional teams, analyze system health, mentor engineers, and promote continuous improvement.

Design, develop, deploy, and support scalable full-stack applications and cloud-native platform services with embedded AI capabilities. Build custom AI agents, RAG systems, LLM integrations, MCP-based tools, and intelligent automation workflows. Own solutions across architecture, implementation, testing, observability, security, deployment, and lifecycle support, with emphasis on reliability for critical manufacturing systems. Collaborate with engineering, product, and business stakeholders while applying strong software engineering and AI-assisted development practices.

Develop and maintain full-stack applications, data analytics, visualization, and reporting solutions for engineering teams. Collaborate with customers and peers to analyze needs, resolve issues, and deliver functionality. Apply modern web, database, cloud, DevOps, CI/CD, testing, observability, and AI-assisted development technologies while emphasizing maintainability, user experience, system quality, and production supportability.

Analyze semiconductor manufacturing and equipment datasets to identify Germhold patterns and root causes. Apply statistical analysis, machine learning, predictive analytics, and data visualization to develop improvement insights, dashboards, and reporting tools. Collaborate with process and equipment engineers to validate findings, evaluate AI-enabled solutions, and recommend ways to improve workstation efficiency, reduce Germholds, and enhance manufacturing capacity.